Summary: Coupling a non invasive brain computer interface with a virtual walking avatar may help those with gait disorders to regain control of their movements, a new study reports. Source: University of Houston.Researchers from the University of Houston have shown for the first time that the use of a brain-computer interface augmented with a virtual walking avatar can control gait, suggesting the protocol may help patients recover the ability to walk after stroke, some spinal cord injuries and certain other gait disabilities.

Researchers said the work, done at the University’s Noninvasive Brain-Machine Interface System Laboratory, is the first to demonstrate that a brain-computer interface can promote and enhance cortical involvement during walking. The study, funded by the National Institute of Neurological Disease and Stroke, was published this week in Scientific Reports.

Researchers already knew electroencephalogram (EEG) readings of brain activity can distinguish whether a subject is standing still or walking. But they hadn’t previously known if a brain-computer interface was practical for helping to promote the ability to walk, or what parts of the brain are relevant to determining gait. NeuroscienceNews.com image is adapted from the U of H video.

Jose Luis Contreras-Vidal, Cullen professor of electrical and computer engineering at UH and senior author of the paper, said the data will be made available to other researchers. While similar work has been done in other primates, this is the first to involve humans, he said. Contreras-Vidal is also site director of the BRAIN Center (Building Reliable Advances and Innovation in Neurotechnology), a National Science Foundation Industry/University Cooperative Research Center.

Contreras-Vidal and researchers with his lab use non-invasive brain monitoring to determine what parts of the brain are involved in an activity, using that information to create an algorithm, or a brain-machine interface, which can translate the subject’s intentions into action.

In addition to Contreras-Vidal, researchers on the project are first author Trieu Phat Luu, a research fellow in neural engineering at UH; Sho Nakagome and Yongtian He, graduate students in the UH Department of Electrical and Computer Engineering.

“Voluntary control of movements is crucial for motor learning and physical rehabilitation,” they wrote. “Our results suggest the possible benefits of using a closed-loop EEG-based BCI-VR (brain-computer interface-virtual reality) system in inducing voluntary control of human gait.”

Researchers already knew electroencephalogram (EEG) readings of brain activity can distinguish whether a subject is standing still or walking. But they hadn’t previously known if a brain-computer interface was practical for helping to promote the ability to walk, or what parts of the brain are relevant to determining gait.

In this case, they collected data from eight healthy subjects, all of whom participated in three trials involving walking on a treadmill while watching an avatar displayed on a monitor. The volunteers were fitted with a 64-channel headset and motion sensors at the hip, knee and ankle joint.

The avatar first was activated by the motion sensors, allowing its movement to precisely mimic that of the test subject. In later tests, the avatar was controlled by the brain-computer interface, meaning the subject controlled the avatar with his or her brain.

The avatar perfectly mimicked the subject’s movements when relying upon the sensors, but the match was less precise when the brain-computer interface was used.

Contreras-Vidal said that’s to be expected, noting that other studies have shown some initial decoding errors as the subject learns to use the interface. “It’s like learning to use a new tool or sport,” he said. “You have to understand how the tool works. The brain needs time to learn that.”

The researchers reported increased activity in the posterior parietal cortex and the inferior parietal lobe, along with increased involvement of the anterior cingulate cortex, which is involved in motor learning and error monitoring.

The next step is to use the protocol with patients, the subject of He’s Ph.D. dissertation.

“The appeal of brain-machine interface is that it places the user at the center of the therapy,” Contreras-Vidal said. “They have to be engaged, because they are in control.”

Abstract

Motor imagery (MI) with neurofeedback has been suggested as promising for motor recovery after stroke. Evidence suggests that regular training facilitates compensatory plasticity, but frequent training is difficult to integrate into everyday life. Using a wireless electroencephalogram (EEG) system, we implemented a frequent and efficient neurofeedback training at the patients’ home. Aiming to overcome maladaptive changes in cortical lateralization patterns we presented a visual feedback, representing the degree of contralateral sensorimotor cortical activity and the degree of sensorimotor cortex lateralization. Three stroke patients practiced every other day, over a period of 4 weeks. Training-related changes were evaluated on behavioral, functional, and structural levels. All 3 patients indicated that they enjoyed the training and were highly motivated throughout the entire training regime. EEG activity induced by MI of the affected hand became more lateralized over the course of training in all three patients. The patient with a significant functional change also showed increased white matter integrity as revealed by diffusion tensor imaging, and a substantial clinical improvement of upper limb motor functions. Our study provides evidence that regular, home-based practice of MI neurofeedback has the potential to facilitate cortical reorganization and may also increase associated improvements of upper limb motor function in chronic stroke patients.

Currently, hand rehabilitation following stroke tends to focus on mildly impaired individuals, partially due to the inability for severely impaired subjects to sufficiently use the paretic hand. Device-assisted interventions offer a means to include this more severe population and show promising behavioral results. However, the ability for this population to demonstrate neural plasticity, a crucial factor in functional recovery following effective post-stroke interventions, remains unclear. This study aimed to investigate neural changes related to hand function induced by a device-assisted task-specific intervention in individuals with moderate to severe chronic stroke (upper extremity Fugl-Meyer < 30). We examined functional cortical reorganization related to paretic hand opening and gray matter (GM) structural changes using a multimodal imaging approach. Individuals demonstrated a shift in cortical activity related to hand opening from the contralesional to the ipsilesional hemisphere following the intervention. This was driven by decreased activity in contralesional primary sensorimotor cortex and increased activity in ipsilesional secondary motor cortex. Additionally, subjects displayed increased GM density in ipsilesional primary sensorimotor cortex and decreased GM density in contralesional primary sensorimotor cortex. These findings suggest that despite moderate to severe chronic impairments, post-stroke participants maintain ability to show cortical reorganization and GM structural changes following a device-assisted task-specific arm/hand intervention. These changes are similar as those reported in post-stroke individuals with mild impairment, suggesting that residual neural plasticity in more severely impaired individuals may have the potential to support improved hand function.

Introduction

Nearly 800,000 people experience a new or recurrent stroke each year in the US (1). Popular therapies, such as constraint-induced movement therapy (CIMT), utilize intense task-specific practice of the affected limb to improve arm/hand function in acute and chronic stroke with mild impairments (2, 3). Neuroimaging results partially attribute the effectiveness of these arm/hand interventions to cortical reorganization in the ipsilesional hemisphere following training in acute and mild chronic stroke (4). Unfortunately, CIMT requires certain remaining functionality in the paretic hand to execute the tasks, and only about 10% of screened patients are eligible (5), thus disqualifying a large population of individuals with moderate to severe impairments. Recently, studies using device-assisted task-specific interventions specifically targeted toward moderate to severe chronic stroke reported positive clinical results (6–8). However, these studies primarily focus on clinical measures, but it is widely accepted that neural plasticity is a key factor for determining outcome (9–11). Consequently, it remains unclear whether moderate to severe chronic stroke [upper extremity Fugl-Meyer Assessment (UEFMA) < 30] maintains the ability to demonstrate neural changes following an arm/hand intervention.

Neural changes induced by task-specific training have been investigated widely using animal models (12). For instance, monkeys or rodents trained on a skilled reach-to-grasp task express enlarged representation of the digits of the hand or forelimb in primary motor cortex (M1) following training as measured by intracortical microstimulation (13, 14). Additionally, rapid local structural changes in the form of dendritic growth, axonal sprouting, myelination, and synaptogenesis occur (15–18). Importantly, both cortical and structural reorganization corresponds to motor recovery following rehabilitative training in these animals (19, 20).

The functional neural mechanisms underlying effective task-specific arm/hand interventions in acute and chronic stroke subjects with mild impairments support those seen in the animal literature described above. Several variations of task-specific combined arm/hand interventions, including CIMT, bilateral task-specific training, and hand-specific robot-assisted practice, have shown cortical reorganization such as increased sensorimotor activity and enlarged motor maps in the ipsilesional hemisphere related to the paretic arm/hand (21–24). These results suggest increased recruitment of residual resources from the ipsilesional hemisphere and/or decreased recruitment of contralesional resources following training. Although the evidence for a pattern of intervention-driven structural changes remains unclear in humans, several groups have shown increases in gray matter (GM) density in sensorimotor cortices (25), along with increases in fractional anisotropy in ipsilesional corticospinal tract (CST) (26) following task-specific training in acute and chronic stroke individuals with mild impairments.

The extensive nature of neural damage in moderate to severe chronic stroke may result in compensatory mechanisms, such as contralesional or secondary motor area recruitment (27). These individuals show increased contralesional activity when moving their paretic arm, which correlates with impairment (28, 29) and may be related to the extent of damage to the ipsilesional CST (30). This suggests that more impaired individuals may increasingly rely on contralesional corticobulbar tracts such as the corticoreticulospinal tract to activate the paretic limb (29). These tracts lack comparable resolution and innervation to the distal parts of the limb, thus sacrificing functionality at the paretic arm/hand (31). Since this population is largely ignored in current arm/hand interventions, it is unknown whether an arm/hand intervention for these more severely impaired post-stroke individuals will increase recruitment of residual ipsilesional corticospinal resources. These ipsilesional CSTs maintain the primary control of hand and finger extensor muscles (32) and are thus crucial for improved hand function. Task-specific training assisted by a device may reengage and strengthen residual ipsilesional corticospinal resources by training distal hand opening together with overall arm use.

The current study seeks to determine whether individuals with moderate to severe chronic stroke maintain the ability to show cortical reorganization and/or structural changes alongside behavioral improvement following a task-specific intervention. We hypothesize that following a device-assisted task-specific intervention, moderate to severe chronic stroke individuals will show similar functional and structural changes as observed in mildly impaired individuals, demonstrated by (i) a shift in cortical activity related to paretic hand opening from the contralesional hemisphere toward the ipsilesional hemisphere and (ii) an increase in GM density in sensorimotor cortices in the ipsilesional hemisphere.[…]

Currently, hand rehabilitation following stroke tends to focus on mildly impaired individuals, partially due to the inability for severely impaired subjects to sufficiently use the paretic hand. Device-assisted interventions offer a means to include this more severe population, and show promising behavioral results. However, the ability for this population to demonstrate neural plasticity, a crucial factor in functional recovery following effective post-stroke interventions, remains unclear. This study aimed to investigate neural changes related to hand function induced by a device-assisted task-specific intervention in individuals with moderate to severe chronic stroke (upper extremity Fugl Meyer < 30). We examined functional cortical reorganization related to paretic hand opening and gray matter structural changes using a multi-modal imaging approach. Individuals demonstrated a shift in cortical activity related to hand opening from the contralesional to the ipsilesional hemisphere following the intervention. This was driven by decreased activity in contralesional primary sensorimotor cortex and increased activity in ipsilesional secondary motor cortex. Additionally, subjects displayed increased gray matter density in ipsilesional primary sensorimotor cortex and decreased gray matter density in contralesional primary sensorimotor cortex. These findings suggest that despite moderate to severe chronic impairments, post-stroke participants maintain ability to show cortical reorganization and gray matter structural changes following a device-assisted task-specific arm/hand intervention. These changes are similar as those reported in post-stroke individuals with mild impairment, suggesting that residual neural plasticity in more severely impaired individuals may have the potential to support improved hand function.

What determines motor recovery in stroke is still unknown and finding markers that could predict and improve stroke recovery is a challenge. In this study, we aimed at understanding the neural mechanisms of motor function recovery after stroke using neurophysiological markers by means of cortical excitability (Transcranial Magnetic Stimulation – TMS) and brain oscillations (electroencephalography – EEG). In this cross-sectional study, fifty-five subjects with chronic stroke (62±14 yo, 17 women, 32±42 months post-stroke) were recruited in two sites. We analyzed TMS measures (i.e. motor threshold – MT – of the affected and unaffected sides) and EEG variables (i.e. power spectrum in different frequency bands and different brain regions of the affected and unaffected hemispheres) and their correlation with motor impairment as measured by Fugl-Meyer. Multiple univariate and multivariate linear regression analyses were performed to identify the predictors of good motor function. A significant interaction effect of MT in the affected hemisphere and power in beta bandwidth over the central region for both affected and unaffected hemispheres was found. We identified that motor function positively correlates with beta rhythm over the central region of the unaffected hemisphere, while it negatively correlates with beta rhythm in the affected hemisphere. Our results suggest that cortical activity in the affected and unaffected hemisphere measured by EEG provides new insights on the association between high frequency rhythms and motor impairment, highlighting the role of excess of beta in the affected central cortical region in poor motor function in stroke recovery.

Introduction

Stroke is a leading cause of morbidity, mortality, and disability worldwide (1, 2). Among the sequels of stroke, motor impairment is one of the most relevant, since it conditions the quality of life of patients, it reduces their capability to perform their daily activities and it impairs their autonomy (3). Despite the advancements of the acute stroke therapy, patients require an intensive rehabilitation program that will partially determine the extent of their recovery (4). These rehabilitation programs aim at stimulating cortical plasticity to improve motor performance and functional recovery (5). However, what determines motor improvement is still unknown. Indeed, finding markers that could predict and enhance stroke recovery is still a challenge (6). Different types of biomarkers exist: diagnostic, prognostic, surrogate outcome, and predictive biomarkers (7). The identification of these biomarkers is critical in the management of stroke patients. In the field of stroke research, great attention has been put to biomarkers found in the serum, especially in acute care. However, research on biomarkers of stroke recovery is still limited, especially using neurophysiological tools.

A critical research area in stroke is to understand the neural mechanisms underlying motor recovery. In this context, neurophysiological techniques such as transcranial magnetic stimulation (TMS) and electroencephalography (EEG) are useful tools that could be used to identify potential biomarkers of stroke recovery. However, there is still limited data to draw further conclusions on neural reorganization in human trials using these techniques. A few studies have shown that, in acute and sub-acute stage, stroke patients present increased power in low frequency bands (i.e., delta and theta bandwidths) in both affected and unaffected sides, as well as increased delta/alpha ratio in the affected brain area; these patterns being also correlated to functional outcome (8–11). Recently, we have identified that, besides TMS-indexed motor threshold (MT), an increased excitability in the unaffected hemisphere, coupled with a decreased excitability in the affected hemisphere, was associated with poor motor function (12), as measured by Fugl-Meyer (FM) [assessing symptoms severity and motor recovery in post-stroke patients with hemiplegia—Fugl-Meyer et al. (13); Gladstone et al. (14)]. However, MT measurement is associated with a poor resolution as it indexes global corticospinal excitability. Therefore, combining this information with direct cortical measures such as cortical oscillations, as measured by EEG, can help us to understand further neural mechanisms of stroke recovery.

To date, there are very few studies looking into EEG and motor recovery. For that reason, we aimed, in the present study, to investigate the relationship between motor impairment, EEG, and TMS variables. To do so, we conducted a prospective multicenter study of patients who had suffered from a stroke, in which we measured functional outcome using FM and performed TMS and EEG recordings. Based on our preliminary work, we expected to identify changes in interhemispheric imbalances on EEG power, especially in frequency bands associated with learning, such as alpha and beta bandwidths. […]

Figure 1. Topoplots showing the topographic distribution of high-beta bandwidth (25 Hz) for every individual. Red areas represent higher high-beta activity, while blue areas represent lower high-beta activity. Central region (C3 or C4) in red stands for the affected side. For patients with poor motor function, a higher beta activity of the affected central region as compared to the affected side is observed in 16 out of 28 individuals. For patients with good motor function, a similar activity over central regions bilaterally, or higher activity over the unaffected central area can be identified in 21 out of 27 individuals. FM = Fugl-Meyer.

Abstract

Background

Transcranial direct current stimulation (tDCS) is a technique for brain modulation that has potential to be used in motor neurorehabilitation. Considering that the cerebellum and motor cortex exert influence on the motor network, their stimulation could enhance motor functions, such as motor imagery, and be utilized for brain-computer interfaces (BCIs) during motor neurorehabilitation.

Methods

A new tDCS montage that influences cerebellum and either right-hand or feet motor area is proposed and validated with a simulation of electric field. The effect of current density (0, 0.02, 0.04 or 0.06 mA/cm2) on electroencephalographic (EEG) classification into rest or right-hand/feet motor imagery was evaluated on 5 healthy volunteers for different stimulation modalities: 1) 10-minutes anodal tDCS before EEG acquisition over right-hand or 2) feet motor cortical area, and 3) 4-seconds anodal tDCS during EEG acquisition either on right-hand or feet cortical areas before each time right-hand or feet motor imagery is performed. For each subject and tDCS modality, analysis of variance and Tukey-Kramer multiple comparisons tests (p <0.001) are used to detect significant differences between classification accuracies that are obtained with different current densities. For tDCS modalities that improved accuracy, t-tests (p <0.05) are used to compare μ and βband power when a specific current density is provided against the case of supplying no stimulation.

Results

The proposed montage improved the classification of right-hand motor imagery for 4 out of 5 subjects when the highest current was applied for 10 minutes over the right-hand motor area. Although EEG band power changes could not be related directly to classification improvement, tDCS appears to affect variably different motor areas on μ and/or β band.

Conclusions

The proposed montage seems capable of enhancing right-hand motor imagery detection when the right-hand motor area is stimulated. Future research should be focused on applying higher currents over the feet motor cortex, which is deeper in the brain compared to the hand motor cortex, since it may allow observation of effects due to tDCS. Also, strategies for improving analysis of EEG respect to accuracy changes should be implemented.

Background

Transcranial direct current stimulation (tDCS) is a noninvasive technique for brain stimulation where direct current is supplied through two or more electrodes in order to modulate temporally brain excitability [1, 2]. This technique has shown potential to improve motor performance and motor learning [3, 4, 5]. Hence, it could be applied in motor neurorehabilitacion [1]. However, tDCS effects vary depending on several factors, such as the size or position of the stimulation electrodes and the current intensity that is applied [6] or the mental state of the user [7]. Therefore, it should be considered that outcomes of tDCS studies are the result of different affected brain networks that may be involved in attention and movements, among other processes.

Volitional locomotion requires automatic control of movement while the cerebral cortex provides commands that are transmitted by neural projections toward the brainstem and the spinal cord. This control involves predictive motor operations that link activity from the cerebral cortex, cerebellum, basal ganglia and brainstem in order to modify actions at the spinal cord level [8]. In general, this set of structures can be considered to form a motor network that allow voluntary movement.

Different parts of the cerebral cortex participate in the performance of self-initiated movement, like the supplementary motor (SMA), the primary motor (M1) and premotor (PM) areas. It is known that M1 is activated during motor execution. Excitatory effects of M1 have been studied with anodal stimulation [6], finding that activation of this region is related to higher motor evoked potentials (MEPs) and an increment of force movement on its associated body part area [9, 10]. Moreover, M1 seems to be critical in the early phase of consolidation of motor skills during procedural motor learning [11], i.e., the implicit skill acquisition through the repeated practice of a task [12].

In addition, the SMA and PM influence M1 in order to program opportune precise motor commands when movement pattern is modified intentionally, based on information from temporoparietal cortices regarding to the body’s state [8]. The SMA contributes in the generation of anticipatory postural adjustments [13]. Consequently, its facilitatory stimulation seems to increase anticipatory postural adjustments amplitudes, to reduce the time required to perform movements during the learning task of sequential movements, and to produce early initiation of motor responses [14, 15, 16]. These studies suggest the possibility of using SMA excitation during treatments for motor disorders, since hemiparesis after stroke involves the impairment of anticipatory motor control at the affected limb [17]. In addition, some studies propose the participation of the SMA in motor memory and both implicit and explicit motor learning [18, 19, 20, 21], i.e, when new information is acquired without intending to do so and when acquisition of skill is conscious [22], respectively. Complimentary to the role of SMA, the PM is crucial for sensory-guided movement initiation and the consolidation of motor sequence learning during sleep [8, 23], while its facilitation with anodal tDCS seems to enhance the excitability from the ipsilateral M1 [24], which may be useful for treatment of PM disorders.

As previously mentioned, the cerebellum is also involved in locomotion through the regulation of motor processes by influencing the cerebral cortex, among other neural structures. During adaptive control of movement, as in the gait process, it seems that loops that interconnect reciprocally motor cortical areas to the basal ganglia and cerebellum allow predictive control of locomotion and they exhibit correlation with movement parameters [8, 25]. Regarding to studies about cerebellar stimulation, there is still not enough knowledge about the effects of tDCS on different neuronal populations and the afferent pathways, so results are variable among studies and their interpretation is more complex than for cerebral tDCS [26]. Furthermore, the topographical motor organization of the cerebellum is not clear yet [27]. Nevertheless, most studies base their experimental procedure on the existence of decussating cerebello-cerebral connections, even if there are also ipsilateral cerebello-cerebral tracts or inter-hemispheric cerebellar connections [28]. Hence, a cerebellar hemisphere is stimulated to affect cerebellar brain inhibition (CBI), which refers to the inherent suppression of cerebellum over the contralateral M1 [29]. For example, the supply of anodal and cathodal stimulation over the right cerebellum in [30] resulted in incremental and decremental CBI on the left M1, respectively. In contrast, there are some studies that suggest this expectation may be not always appropriate. In [31] it was shown that inhibitory transcranial magnetic stimulation (a stimulation technique that provides magnetic field pulses on the brain [32]) over the lateral right cerebellum led to procedural learning decrement for tasks performed with either the right or left hand, whereas inhibition of lateral left cerebellar hemisphere decreased learning only with the left hand. In addition, results from [33] showed that cathodal cerebellar tDCS worsened locomotor adaptation ipsilaterally. These two studies may provide a reference for using cerebellar inhibition for avoiding undesired brain activity changes during motor rehabilitation, such as compensatory movement habits that might contribute to maladaptative plasticity and hamper the goal of achieving a normal movement pattern [34]. […]

Fig. 1 tDCS montage. Scheme of tDCS electrodes position in reference to EEG electrodes and inion (left), and placement of tDCS electrodes on the EEG cap (right). Electrodes 1,2 and 3 are highlighted in red, green and blue, respectively

BRAINTRAIN will improve and adapt the methods of real-time fMRI neurofeedback (fMRI-NF) for clinical use, including the combination with electroencephalography (EEG) and the development of standardised procedures for the mapping of brain networks that can be targeted with neurofeedback.

Its core component will be the exploration of the efficacy of fMRI-NF in selected mental and neurodevelopmental disorders that involve motivational, emotional and social neural systems. The ultimate goals of BRAINTRAIN are therefore to :

Develop new or optimize existing imaging technologies,

Validate their application as a therapeutic tool to mental and behavioural disorders by integrating imaging data with complementarity knowledge resulting bioinformatics and clinical data,

Allow the diagnosis of mental disorders at the pre-symptomatic stage or early during development,

Better measure disease progression.

Develop transfer technologies for fMRI-NF through EEG and serious games.

BRAINTRAIN is innovative in the development of new real-time imaging technologies e.g. new sequences, image reconstruction methods and data analysis software. This will also be the first clinical testing of fMRI-NF in a set of disorders with extraordinary socioeconomic and public health impact.

The project started in November 2013 and will last four years. It is coordinated by Cardiff University (Professor David Linden, Wales, UK).

BRAINTRAIN is a European research network (Collaborative Project) supported by the European Commission under the Health Cooperation Work Programme of the 7th Framework Programme, under the Grant Agreement n°602186.

Estimation of fatigue is a required criteria in the field of physiology. The estimation of muscle fatigue and its development in the brain signals can provide a level of endurance among athletes and limits of a persons in doing physical tasks. In this paper a technique for detecting and estimating the fatigue development using regression parameters for EEG signals is discussed. The study of 14 subjects was undertaken and analysed for the fatigue development using Auto-Regression(AR) model. The behaviour of the error function obtained is analysed for the prediction of the stages and limits of muscle fatigue development.

I. Introduction

Muscle fatigue is a phenomenon associated with the muscle contraction. It is understood as the reduction in the ability of maximal force generation by the muscle with time, during its stressing, as the muscle contraction keeps on increasing. The nervous system’s limitation to generate sustainable signals and the reduction of ability of muscle fiber to contract are two major factors contributing to fatigue development [1]. Fatigue development limits the performance and capability of the individual in sports, long stretch driving conditions and in rigourous day to day activities. Hence a parameter that can estimate the fatigue levels and provide a break point for maximum fatigue can be useful for physiology and in other areas such as labour. People working under mines can be monitored for the fatigue break point and the overall productivity of such areas can be increased by proper analysis. The fatigue development in a person can be analysed via number of methods based on physiological changes. These include Electroencephalogram (EEG), Elec-tromyography(EMG), and Heart Rate Variability(HRV). Zadry et.al. [2] reported the increase in alpha band power level of EEG with time for fatigue development [3]. Ali et.al. also reported increase in RMS values of different bands in EEG [4]. Few studies measure brain activity in light repetitive task using EEG [5] to measure drowsiness or fatigue on drivers [6] [7] and night work [8] [9]. The EEG analysis for overall fatigue has been the focus of research, but research for specific muscle fatigue detection has been limited. The EEG based detection of fatigue has the advantage of quantitative based assessment. But, for real time application perspective faster computational power and signal processing methods are required. One of the challenges based on EEG based approach is the disturbances and contamination of the signal from eyes blinking action, muscle noise by movements and instrumental noises like line noise, electronic interferences [10]. Another problem is imposed by the inter-variability and intra-variability in EEG dynamics accompanying loss of alertness [11].

Skill acquisition requires distributed learning both within (online) and across (offline) days to consolidate experiences into newly learned abilities. In particular, piloting an aircraft requires skills developed from extensive training and practice. Here, we tested the hypothesis that transcranial direct current stimulation (tDCS) can modulate neuronal function to improve skill learning and performance during flight simulator training of aircraft landing procedures.

Thirty-two right-handed participants consented to participate in four consecutive daily sessions of flight simulation training and received sham or anodal high-definition-tDCS to the right dorsolateral prefrontal cortex (DLPFC) or left motor cortex (M1) in a randomized, double-blind experiment. Continuous electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS) were collected during flight simulation, n-back working memory, and resting-state assessments. tDCS of the right DLPFC increased midline-frontal theta-band activity in flight and n-back working memory training, confirming tDCS-related modulation of brain processes involved in executive function. This modulation corresponded to a significantly different online and offline learning rates for working memory accuracy and decreased inter-subject behavioral variability in flight and n-back tasks in the DLPFC stimulation group. Additionally, tDCS of left M1 increased parietal alpha power during flight tasks and tDCS to the right DLPFC increased midline frontal theta-band power during n-back and flight tasks.

These results demonstrate a modulation of group variance in skill acquisition through an increasing in learned skill consistency in cognitive and real-world tasks with tDCS. Further, tDCS performance improvements corresponded to changes in electrophysiological and blood-oxygenation activity of the DLPFC and motor cortices, providing a stronger link between modulated neuronal function and behavior.

Figure 1. Experimental design. (A) Experiment timeline depicting the relative timing of each task (see Table 1 for descriptions of each task). The N-Back and Easy Landing tasks are highlighted, and the duration of tDCS is depicted in red. (B) An example of 6 trials of the N-Back task is shown. 1-back orientation and location match trials are highlighted in yellow. (C) The flight simulator, neuroimaging (EEG and FNIRS) and tDCS setup is shown with on a subject (1). Flight simulator equipment includes three-panel display, a radio panel (2), an instrument panel (3) with (from left to right) compass, altimeter, airspeed indicator, vertical speed indicator, and turn/slip indicator, a multi-panel (4) with (from left to right) autopilot settings, auto throttle switch, flaps switch, and elevator trim wheel, yoke (5), and throttle quadrant system (6). (D) Autopilot flight path for the Easy Landing task is shown in 3 dimensions, color-coded by vertical speed. Screenshots for initial descent, approach, and landing are also shown.

One of the recent trends in gait rehabilitation is to incorporate bio-signals, such as electromyography (EMG) or electroencephalography (EEG), for facilitating neuroplasticity, i.e. top-down approach. In this study, we investigated decoding stroke patients’ gait intention through a wireless EEG system. To overcome patient-specific EEG patterns due to impaired cerebral cortices, common spatial patterns (CSP) was employed. We demonstrated that CSP filter can be used to maximize the EEG signal variance-ratio of gait and standing conditions. Finally, linear discriminant analysis (LDA) classification was conducted, whereby the average accuracy of 73.2% and the average delay of 0.13 s were achieved for 3 chronic stroke patients. Additionally, we also found out that the inverse CSP matrix topography of stroke patients’ EEG showed good agreement with the patients’ paretic side.